时间：2015年6月18（周四）14：00-16：00地点：学院南路校区，学术会堂702

MicrosoftInternetExplorer402DocumentNotSpecified7.8 磅Normal0报告一：Almost everything you always wanted to know about nonlinear state space models (but were afraid to ask)

报告人：Professor David Stoffer, Department of Statistics, University of Pittsburgh.

摘要：

Ever wonder how the "Mars One" one-way trip to Mars will actually get to the planet without winding up on, say Venus? The tracking devices will use a nonlinear state space model. While inference for the linear Gaussian model is fairly simple, inference for nonlinear models can be difficult and often relies on derivative free numerical optimization techniques. A promising method that I will discuss is based on particle approximations of the conditional distribution of the hidden process given the data. This distribution is needed for both classical inference (e.g., Monte Carlo EM type algorithms) and Bayesian inference (e.g., Gibbs sampler). Particle methods are an extension of sequential importance sampling (SIS). Although the SIS algorithm has been known since the early 1970s, its use in nonlinear problems remained largely unnoticed until the early 1990s. Obviously the available computational power was too limited to allow convincing applications of these methods, but other difficulties plagued the technique. Time series data are typically long and particles have a tendency to die young. Consequently, the approach is cursed by dimensionality. But as Shakespeare noted, if dimensionality curseth, a better algorithm useth.

报告二：Testing for structural breaks in the presence of data perturbations: impacts and wavelet-based improvements

报告人：Dr. Yushu Li, Norwegian School of Economics

摘要：

Stationary is an essential assumption in the traditional time series research. However, in empirical situations, the structure break occurs often in certain time series. The test for structural break in time series is an important research topic. This paper investigates how classical measurement error and additive outliers (AO) influence tests for structural change based on F-statistics. We derive theoretically the impact of general additive disturbances in the regressors on the asymptotic distribution of these tests for structural change. The small sample properties in the case of classical measurement error and AO are investigated via Monte Carlo simulations, revealing that sizes are biased upwards and that powers are reduced. The wavelet-based denoising methods are used to reduce these distortions. We show that these methods can significantly improve the performance of structural break tests.